Background: DNA-methylation-based machine learning algorithms have demonstrated powerful diagnostic capabilities, and these tools are currently emerging in many fields of tumor diagnosis and patient prognosis prediction. This work aimed to identify novel DNA methylation diagnostic biomarkers for differentiating cervical cancer (CC) from normal tissues, as well as a prognostic prediction model to predict survival of CC patients. Methods: The methylation profiles with the available clinical characteristics were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) program. We first screened out the differential methylation sites in CC and normal tissues and performed multiple statistical analyses to discover DNA methylation diagnostic markers that are used to distinguish CC and normal control. Then, we developed a methylation-based survival model to improve risk stratification. Results: A diagnostic prediction panel consists of five CpG markers that could predict cervical cancer versus normal tissue with highly correct rate of 100%, and cg16428251, cg22341310, and cg23316360 which in diagnostic prediction panel all could yield high sensitivity and specificity for detection of CC and normal in six cohorts (area under curve [AUC] > 0.8), in addition to excellent performance in discriminating between CC and normal sample. The diagnostic marker panel also effectively predicted the CIN3 versus normal tissue with high accuracy in two datasets (AUC = 0.80, 0.789, respectively). Furthermore, a prognostic prediction model aggregated two CpG markers that effectively stratified the prognosis of high-risk and low-risk groups (training cohort: hazard ratio [HR] 4, 95% CI: 1.7-9.6, P = 0.0021; testing cohort: hazard ratio [HR] 1.9, 95% CI: 1.2-3.1, P = 0.0072). Conclusion:The findings of our study showed that DNA methylation markers are of great value in the diagnosis and prognosis of CC.

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Background: Current international prognostic index is widely questioned on the risk stratification of peripheral T-cell lymphoma and do not accurately predict the outcome for patients. We postulated that multiple mRNAs could combined into a single model to improve risk stratification and to guide Clinicians implementing personalized therapeutic regimen for these patients. Methods: The gene expression profiles with clinical characteristics were selected and downloaded from the Gene Expression Omnibus (GEO) database. weighted gene co-expression network analysis (WGCNA) was used to screening genes in selected module which most closely related to PTCLs. Then build a gene classifier using a Lasso Cox Regression model and validated the prognostic accuracy of this mRNA signature in an internal validation cohort. Finally, a prognostic nomogram was constructed and performance was assessed by calibration plot and the concordance index (C-index). Results: 799 WGCNA-selected mRNAs in black module were identified and a mRNA signature which based on DOCK2, GSTM1, H2AFY, KCNAB2, LAPTM5 and SYK for PTCLs was developed. Significantly statistical difference can be seen in overall survival of PTCLs between low risk group and high risk group(training set :hazard ratio [HR] 4.3, 95% CI 2.4–7.4, p<0·0001; internal testing set :hazard ratio [HR] 2.4, 95% CI 1.2–4.8, p<0·01).Multivariate regression demonstrated that the signature was an independently prognostic factor contrast to age and gender. Furthermore, receiver operating characteristic analysis indicated that this signature exhibited excellent diagnostic efficiency for overall survival. Moreover, the nomogram which combined the six-genes risk signature and multiple clinical factors suggesting that predicted survival probability agreed well with the actual survival probability. Conclusions: The signature is a reliable prognostic tool for patients with PTCLs and it has the potential for clinicians to implement personalized therapeutic regimen for patients with stage PTCLs.